Overview

Dataset statistics

Number of variables28
Number of observations2500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory547.0 KiB
Average record size in memory224.1 B

Variable types

Categorical16
Numeric12

Alerts

csf_glucose_mg has constant value ""Constant
headache is highly imbalanced (54.0%)Imbalance
cd4 is highly imbalanced (53.9%)Imbalance
wbc is highly imbalanced (77.6%)Imbalance
days_art has 1174 (47.0%) zerosZeros

Reproduction

Analysis started2024-04-10 19:48:10.829468
Analysis finished2024-04-10 19:48:54.587352
Duration43.76 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

age
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
0.0
2006 
1.0
494 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2006
80.2%
1.0 494
 
19.8%

Length

2024-04-10T22:48:54.863060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:48:55.324506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2006
80.2%
1.0 494
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 4506
60.1%
. 2500
33.3%
1 494
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4506
90.1%
1 494
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4506
60.1%
. 2500
33.3%
1 494
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4506
60.1%
. 2500
33.3%
1 494
 
6.6%

sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
1310 
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1310
52.4%
0.0 1190
47.6%

Length

2024-04-10T22:48:55.574039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:48:55.818129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1310
52.4%
0.0 1190
47.6%

Most occurring characters

ValueCountFrequency (%)
0 3690
49.2%
. 2500
33.3%
1 1310
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3690
73.8%
1 1310
 
26.2%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3690
49.2%
. 2500
33.3%
1 1310
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3690
49.2%
. 2500
33.3%
1 1310
 
17.5%

on_art
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
0.0
1421 
1.0
1079 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1421
56.8%
1.0 1079
43.2%

Length

2024-04-10T22:48:56.084482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:48:56.322930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1421
56.8%
1.0 1079
43.2%

Most occurring characters

ValueCountFrequency (%)
0 3921
52.3%
. 2500
33.3%
1 1079
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3921
78.4%
1 1079
 
21.6%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3921
52.3%
. 2500
33.3%
1 1079
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3921
52.3%
. 2500
33.3%
1 1079
 
14.4%

days_art
Real number (ℝ)

ZEROS 

Distinct824
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.63
Minimum0
Maximum4199
Zeros1174
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:48:56.616620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q3577
95-th percentile2418.05
Maximum4199
Range4199
Interquartile range (IQR)577

Descriptive statistics

Standard deviation838.32848
Coefficient of variation (CV)1.7965593
Kurtosis4.3565098
Mean466.63
Median Absolute Deviation (MAD)10
Skewness2.1745742
Sum1166575
Variance702794.64
MonotonicityNot monotonic
2024-04-10T22:48:56.999101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1174
47.0%
11 16
 
0.6%
9 13
 
0.5%
4 12
 
0.5%
80 12
 
0.5%
7 12
 
0.5%
10 12
 
0.5%
15 12
 
0.5%
20 11
 
0.4%
335 9
 
0.4%
Other values (814) 1217
48.7%
ValueCountFrequency (%)
0 1174
47.0%
2 4
 
0.2%
3 8
 
0.3%
4 12
 
0.5%
5 6
 
0.2%
6 7
 
0.3%
7 12
 
0.5%
8 7
 
0.3%
9 13
 
0.5%
10 12
 
0.5%
ValueCountFrequency (%)
4199 1
< 0.1%
4149 1
< 0.1%
4105 1
< 0.1%
4095 1
< 0.1%
4042 1
< 0.1%
4024 1
< 0.1%
4021 1
< 0.1%
3993 1
< 0.1%
3976 1
< 0.1%
3956 1
< 0.1%

temp
Real number (ℝ)

Distinct1090
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.786191
Minimum34.108662
Maximum39.49784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:48:57.368792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum34.108662
5-th percentile35.291977
Q136.786191
median36.786191
Q336.786191
95-th percentile38.468001
Maximum39.49784
Range5.3891782
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83021177
Coefficient of variation (CV)0.022568571
Kurtosis2.2609838
Mean36.786191
Median Absolute Deviation (MAD)0
Skewness0.25018637
Sum91965.478
Variance0.68925158
MonotonicityNot monotonic
2024-04-10T22:48:57.752879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.78619131 1401
56.0%
35.90000033 2
 
0.1%
35.90000031 2
 
0.1%
37.2000001 2
 
0.1%
36.59999915 2
 
0.1%
35.90000118 2
 
0.1%
35.90000123 2
 
0.1%
38.20000072 2
 
0.1%
38.20000049 2
 
0.1%
37.2000006 2
 
0.1%
Other values (1080) 1081
43.2%
ValueCountFrequency (%)
34.10866231 1
< 0.1%
34.11097199 1
< 0.1%
34.11456313 1
< 0.1%
34.12320716 1
< 0.1%
34.12477316 1
< 0.1%
34.12522576 1
< 0.1%
34.15957181 1
< 0.1%
34.16460264 1
< 0.1%
34.18154067 1
< 0.1%
34.18697491 1
< 0.1%
ValueCountFrequency (%)
39.49784048 1
< 0.1%
39.47138576 1
< 0.1%
39.4686203 1
< 0.1%
39.4669508 1
< 0.1%
39.46289554 1
< 0.1%
39.45752755 1
< 0.1%
39.4566103 1
< 0.1%
39.45546645 1
< 0.1%
39.42697686 1
< 0.1%
39.40254161 1
< 0.1%

gcs
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
1780 
0.0
720 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1780
71.2%
0.0 720
28.8%

Length

2024-04-10T22:48:58.081653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:48:58.324690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1780
71.2%
0.0 720
28.8%

Most occurring characters

ValueCountFrequency (%)
0 3220
42.9%
. 2500
33.3%
1 1780
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3220
64.4%
1 1780
35.6%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3220
42.9%
. 2500
33.3%
1 1780
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3220
42.9%
. 2500
33.3%
1 1780
23.7%

weight
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
1540 
0.0
960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1540
61.6%
0.0 960
38.4%

Length

2024-04-10T22:48:58.575217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:48:58.808993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1540
61.6%
0.0 960
38.4%

Most occurring characters

ValueCountFrequency (%)
0 3460
46.1%
. 2500
33.3%
1 1540
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3460
69.2%
1 1540
30.8%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3460
46.1%
. 2500
33.3%
1 1540
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3460
46.1%
. 2500
33.3%
1 1540
20.5%

fever
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
0.0
1605 
1.0
895 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1605
64.2%
1.0 895
35.8%

Length

2024-04-10T22:48:59.066882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:48:59.307197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1605
64.2%
1.0 895
35.8%

Most occurring characters

ValueCountFrequency (%)
0 4105
54.7%
. 2500
33.3%
1 895
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4105
82.1%
1 895
 
17.9%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4105
54.7%
. 2500
33.3%
1 895
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4105
54.7%
. 2500
33.3%
1 895
 
11.9%

dur_fever
Real number (ℝ)

Distinct76
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.432129
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:48:59.606008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median26.432129
Q326.432129
95-th percentile68
Maximum99
Range98
Interquartile range (IQR)9.4321285

Descriptive statistics

Standard deviation17.823795
Coefficient of variation (CV)0.67432312
Kurtosis5.3973883
Mean26.432129
Median Absolute Deviation (MAD)0
Skewness2.0863755
Sum66080.321
Variance317.68768
MonotonicityNot monotonic
2024-04-10T22:48:59.984026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.43212851 1255
50.2%
14 211
 
8.4%
30 166
 
6.6%
7 166
 
6.6%
21 82
 
3.3%
2 40
 
1.6%
4 38
 
1.5%
3 34
 
1.4%
10 33
 
1.3%
11 33
 
1.3%
Other values (66) 442
 
17.7%
ValueCountFrequency (%)
1 16
 
0.6%
2 40
 
1.6%
3 34
 
1.4%
4 38
 
1.5%
5 22
 
0.9%
6 9
 
0.4%
7 166
6.6%
8 6
 
0.2%
9 5
 
0.2%
10 33
 
1.3%
ValueCountFrequency (%)
99 4
 
0.2%
98 1
 
< 0.1%
97 11
 
0.4%
96 5
 
0.2%
95 5
 
0.2%
94 6
 
0.2%
93 6
 
0.2%
92 3
 
0.1%
91 9
 
0.4%
90 31
1.2%

headache
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
2257 
0.0
243 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2257
90.3%
0.0 243
 
9.7%

Length

2024-04-10T22:49:00.310523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:00.543782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2257
90.3%
0.0 243
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 2743
36.6%
. 2500
33.3%
1 2257
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2743
54.9%
1 2257
45.1%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2743
36.6%
. 2500
33.3%
1 2257
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2743
36.6%
. 2500
33.3%
1 2257
30.1%

dur_headache
Real number (ℝ)

Distinct75
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.067735
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:00.842088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q110
median14
Q330
95-th percentile71.15
Maximum99
Range97
Interquartile range (IQR)20

Descriptive statistics

Standard deviation21.468232
Coefficient of variation (CV)0.89199218
Kurtosis3.0559342
Mean24.067735
Median Absolute Deviation (MAD)7
Skewness1.8126979
Sum60169.339
Variance460.88498
MonotonicityNot monotonic
2024-04-10T22:49:01.646167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 526
21.0%
30 511
20.4%
7 348
13.9%
21 242
9.7%
60 143
 
5.7%
10 107
 
4.3%
3 67
 
2.7%
4 67
 
2.7%
5 46
 
1.8%
2 41
 
1.6%
Other values (65) 402
16.1%
ValueCountFrequency (%)
2 41
 
1.6%
3 67
 
2.7%
4 67
 
2.7%
5 46
 
1.8%
6 15
 
0.6%
7 348
13.9%
8 4
 
0.2%
9 24
 
1.0%
10 107
 
4.3%
11 6
 
0.2%
ValueCountFrequency (%)
99 15
0.6%
98 7
0.3%
97 12
0.5%
96 4
 
0.2%
95 13
0.5%
94 9
0.4%
93 5
 
0.2%
92 12
0.5%
91 13
0.5%
90 16
0.6%

vischange
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
0.0
1698 
1.0
802 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1698
67.9%
1.0 802
32.1%

Length

2024-04-10T22:49:02.013604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:02.250440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1698
67.9%
1.0 802
32.1%

Most occurring characters

ValueCountFrequency (%)
0 4198
56.0%
. 2500
33.3%
1 802
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4198
84.0%
1 802
 
16.0%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4198
56.0%
. 2500
33.3%
1 802
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4198
56.0%
. 2500
33.3%
1 802
 
10.7%

dur_vischange
Real number (ℝ)

Distinct50
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7264
Minimum0
Maximum60
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:02.538437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median8
Q314
95-th percentile33
Maximum60
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.594057
Coefficient of variation (CV)0.83244725
Kurtosis4.7366215
Mean12.7264
Median Absolute Deviation (MAD)5
Skewness1.9232989
Sum31816
Variance112.23404
MonotonicityNot monotonic
2024-04-10T22:49:02.913102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 591
23.6%
14 519
20.8%
21 240
9.6%
5 185
 
7.4%
4 150
 
6.0%
3 143
 
5.7%
30 97
 
3.9%
10 81
 
3.2%
1 67
 
2.7%
2 56
 
2.2%
Other values (40) 371
14.8%
ValueCountFrequency (%)
0 4
 
0.2%
1 67
 
2.7%
2 56
 
2.2%
3 143
 
5.7%
4 150
 
6.0%
5 185
 
7.4%
6 36
 
1.4%
7 591
23.6%
8 28
 
1.1%
9 28
 
1.1%
ValueCountFrequency (%)
60 13
0.5%
59 6
0.2%
58 6
0.2%
57 8
0.3%
56 9
0.4%
54 5
 
0.2%
52 1
 
< 0.1%
50 1
 
< 0.1%
44 3
 
0.1%
43 1
 
< 0.1%

vomit
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
1359 
0.0
1141 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1359
54.4%
0.0 1141
45.6%

Length

2024-04-10T22:49:03.250700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:03.492796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1359
54.4%
0.0 1141
45.6%

Most occurring characters

ValueCountFrequency (%)
0 3641
48.5%
. 2500
33.3%
1 1359
 
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3641
72.8%
1 1359
 
27.2%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3641
48.5%
. 2500
33.3%
1 1359
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3641
48.5%
. 2500
33.3%
1 1359
 
18.1%

dur_vomit
Real number (ℝ)

Distinct46
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8636
Minimum0
Maximum60
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:03.780037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q314
95-th percentile30
Maximum60
Range60
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.4757613
Coefficient of variation (CV)0.95624367
Kurtosis4.5889095
Mean8.8636
Median Absolute Deviation (MAD)4
Skewness1.8867487
Sum22159
Variance71.83853
MonotonicityNot monotonic
2024-04-10T22:49:04.132292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7 501
20.0%
14 423
16.9%
1 255
10.2%
4 255
10.2%
3 242
9.7%
2 215
8.6%
5 196
 
7.8%
30 98
 
3.9%
21 86
 
3.4%
6 54
 
2.2%
Other values (36) 175
 
7.0%
ValueCountFrequency (%)
0 4
 
0.2%
1 255
10.2%
2 215
8.6%
3 242
9.7%
4 255
10.2%
5 196
 
7.8%
6 54
 
2.2%
7 501
20.0%
8 12
 
0.5%
9 3
 
0.1%
ValueCountFrequency (%)
60 3
0.1%
56 1
 
< 0.1%
54 2
0.1%
53 2
0.1%
52 3
0.1%
51 1
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
40 3
0.1%
37 2
0.1%

cd4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
0.0
2256 
1.0
244 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2256
90.2%
1.0 244
 
9.8%

Length

2024-04-10T22:49:04.456860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:04.685980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2256
90.2%
1.0 244
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 4756
63.4%
. 2500
33.3%
1 244
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4756
95.1%
1 244
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4756
63.4%
. 2500
33.3%
1 244
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4756
63.4%
. 2500
33.3%
1 244
 
3.3%

wbc
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
0.0
2410 
1.0
 
90

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2410
96.4%
1.0 90
 
3.6%

Length

2024-04-10T22:49:04.950314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:05.179770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2410
96.4%
1.0 90
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 4910
65.5%
. 2500
33.3%
1 90
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4910
98.2%
1 90
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4910
65.5%
. 2500
33.3%
1 90
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4910
65.5%
. 2500
33.3%
1 90
 
1.2%

hemoglobin
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
1445 
0.0
1055 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1445
57.8%
0.0 1055
42.2%

Length

2024-04-10T22:49:05.430030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:05.660612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1445
57.8%
0.0 1055
42.2%

Most occurring characters

ValueCountFrequency (%)
0 3555
47.4%
. 2500
33.3%
1 1445
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3555
71.1%
1 1445
28.9%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3555
47.4%
. 2500
33.3%
1 1445
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3555
47.4%
. 2500
33.3%
1 1445
19.3%

platelets
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
2073 
0.0
427 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2073
82.9%
0.0 427
 
17.1%

Length

2024-04-10T22:49:05.911505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:06.146690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2073
82.9%
0.0 427
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 2927
39.0%
. 2500
33.3%
1 2073
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2927
58.5%
1 2073
41.5%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2927
39.0%
. 2500
33.3%
1 2073
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2927
39.0%
. 2500
33.3%
1 2073
27.6%

sodium
Real number (ℝ)

Distinct38
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.6108
Minimum0
Maximum148
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:06.418341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121
Q1127
median130
Q3135
95-th percentile141
Maximum148
Range148
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.5816565
Coefficient of variation (CV)0.0580477
Kurtosis104.44292
Mean130.6108
Median Absolute Deviation (MAD)4
Skewness-6.071343
Sum326527
Variance57.481516
MonotonicityNot monotonic
2024-04-10T22:49:06.763412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
128 247
 
9.9%
130 212
 
8.5%
127 164
 
6.6%
131 163
 
6.5%
133 157
 
6.3%
134 141
 
5.6%
132 136
 
5.4%
137 130
 
5.2%
129 125
 
5.0%
138 107
 
4.3%
Other values (28) 918
36.7%
ValueCountFrequency (%)
0 3
 
0.1%
112 8
 
0.3%
113 16
0.6%
114 7
 
0.3%
115 2
 
0.1%
116 8
 
0.3%
117 2
 
0.1%
118 7
 
0.3%
119 6
 
0.2%
120 33
1.3%
ValueCountFrequency (%)
148 11
 
0.4%
147 9
 
0.4%
146 16
 
0.6%
145 28
1.1%
144 28
1.1%
143 15
 
0.6%
142 6
 
0.2%
141 24
1.0%
140 19
0.8%
139 43
1.7%

potassium
Real number (ℝ)

Distinct1511
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0599634
Minimum2.4147184
Maximum6.5940444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:07.132223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.4147184
5-th percentile3.1155245
Q13.8272486
median4.0599634
Q34.2670071
95-th percentile4.9622841
Maximum6.5940444
Range4.179326
Interquartile range (IQR)0.43975846

Descriptive statistics

Standard deviation0.54997116
Coefficient of variation (CV)0.1354621
Kurtosis1.967185
Mean4.0599634
Median Absolute Deviation (MAD)0.21978714
Skewness0.36719323
Sum10149.908
Variance0.30246828
MonotonicityNot monotonic
2024-04-10T22:49:07.471684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.05996335 839
33.6%
3.7 19
 
0.8%
3.70000001 17
 
0.7%
3.70000003 17
 
0.7%
3.70000002 16
 
0.6%
3.70000004 16
 
0.6%
3.90000007 12
 
0.5%
3.90000001 10
 
0.4%
3.90000006 9
 
0.4%
3.9 9
 
0.4%
Other values (1501) 1536
61.4%
ValueCountFrequency (%)
2.41471838 1
< 0.1%
2.42461349 1
< 0.1%
2.42694203 1
< 0.1%
2.43928409 1
< 0.1%
2.4486286 1
< 0.1%
2.46022672 1
< 0.1%
2.49029053 1
< 0.1%
2.49667685 1
< 0.1%
2.5038628 1
< 0.1%
2.51436907 1
< 0.1%
ValueCountFrequency (%)
6.59404442 1
< 0.1%
6.54775973 1
< 0.1%
6.46287872 1
< 0.1%
6.45575378 1
< 0.1%
6.40971492 1
< 0.1%
6.40147523 1
< 0.1%
6.38743893 1
< 0.1%
6.21826332 1
< 0.1%
6.21213255 1
< 0.1%
6.17617521 1
< 0.1%

csf_lactate
Real number (ℝ)

Distinct87
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1954
Minimum1
Maximum10.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:07.815228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3.1
Q34.6
95-th percentile7.205
Maximum10.1
Range9.1
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.2082621
Coefficient of variation (CV)0.69107532
Kurtosis-0.20974314
Mean3.1954
Median Absolute Deviation (MAD)2.1
Skewness0.74025094
Sum7988.5
Variance4.8764214
MonotonicityNot monotonic
2024-04-10T22:49:08.165700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 950
38.0%
3.8 84
 
3.4%
4.1 58
 
2.3%
3.2 52
 
2.1%
3.5 50
 
2.0%
3.3 49
 
2.0%
3.1 45
 
1.8%
3.7 43
 
1.7%
3.4 40
 
1.6%
5.9 40
 
1.6%
Other values (77) 1089
43.6%
ValueCountFrequency (%)
1 950
38.0%
1.4 8
 
0.3%
1.5 10
 
0.4%
1.6 4
 
0.2%
1.7 2
 
0.1%
1.8 3
 
0.1%
1.9 5
 
0.2%
2 17
 
0.7%
2.1 27
 
1.1%
2.2 22
 
0.9%
ValueCountFrequency (%)
10.1 2
 
0.1%
9.9 8
0.3%
9.8 4
0.2%
9.7 1
 
< 0.1%
9.6 2
 
0.1%
9.5 2
 
0.1%
9.4 5
0.2%
9.3 2
 
0.1%
9.2 7
0.3%
9.1 7
0.3%

csf_glucose_mg
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
2500 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2500
100.0%

Length

2024-04-10T22:49:08.483462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:08.706023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2500
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2500
33.3%
. 2500
33.3%
0 2500
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2500
50.0%
0 2500
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2500
33.3%
. 2500
33.3%
0 2500
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2500
33.3%
. 2500
33.3%
0 2500
33.3%

csf_lymph
Real number (ℝ)

Distinct70
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.184532
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:08.985787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile39
Q170
median75.184532
Q379
95-th percentile100
Maximum100
Range98
Interquartile range (IQR)9

Descriptive statistics

Standard deviation15.465355
Coefficient of variation (CV)0.20569862
Kurtosis3.3079161
Mean75.184532
Median Absolute Deviation (MAD)4.8154681
Skewness-1.0624786
Sum187961.33
Variance239.17719
MonotonicityNot monotonic
2024-04-10T22:49:09.366890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.18453189 1026
41.0%
100 314
 
12.6%
70 222
 
8.9%
75 150
 
6.0%
65 123
 
4.9%
80 112
 
4.5%
60 58
 
2.3%
90 40
 
1.6%
30 37
 
1.5%
32 31
 
1.2%
Other values (60) 387
 
15.5%
ValueCountFrequency (%)
2 4
0.2%
4 2
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 2
0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
100 314
12.6%
99 4
 
0.2%
98 6
 
0.2%
97 5
 
0.2%
96 15
 
0.6%
95 18
 
0.7%
94 6
 
0.2%
92 13
 
0.5%
91 1
 
< 0.1%
90 40
 
1.6%

csf_amt_removed
Real number (ℝ)

Distinct50
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.584
Minimum0
Maximum49
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:09.724973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median12
Q320
95-th percentile30
Maximum49
Range49
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.8385854
Coefficient of variation (CV)0.60604672
Kurtosis0.58397424
Mean14.584
Median Absolute Deviation (MAD)5
Skewness0.86586578
Sum36460
Variance78.120592
MonotonicityNot monotonic
2024-04-10T22:49:10.094010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 273
 
10.9%
11 158
 
6.3%
8 145
 
5.8%
15 134
 
5.4%
12 128
 
5.1%
6 108
 
4.3%
16 104
 
4.2%
20 103
 
4.1%
30 100
 
4.0%
14 91
 
3.6%
Other values (40) 1156
46.2%
ValueCountFrequency (%)
0 3
 
0.1%
1 63
2.5%
2 62
2.5%
3 56
 
2.2%
4 56
 
2.2%
5 88
3.5%
6 108
4.3%
7 66
2.6%
8 145
5.8%
9 55
 
2.2%
ValueCountFrequency (%)
49 3
0.1%
48 3
0.1%
47 4
0.2%
46 3
0.1%
45 2
0.1%
44 3
0.1%
43 1
 
< 0.1%
42 2
0.1%
41 2
0.1%
40 4
0.2%

viralload
Real number (ℝ)

Distinct1201
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354595.27
Minimum5.2451017
Maximum2987757.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-04-10T22:49:10.446567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5.2451017
5-th percentile141.74299
Q1200269.16
median354595.27
Q3354595.27
95-th percentile761840.63
Maximum2987757.9
Range2987752.6
Interquartile range (IQR)154326.11

Descriptive statistics

Standard deviation375304.8
Coefficient of variation (CV)1.0584033
Kurtosis20.796239
Mean354595.27
Median Absolute Deviation (MAD)0
Skewness4.0329897
Sum8.8648817 × 108
Variance1.4085369 × 1011
MonotonicityNot monotonic
2024-04-10T22:49:10.823692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
354595.2691 1300
52.0%
790410.381 1
 
< 0.1%
228.9683969 1
 
< 0.1%
260799.3659 1
 
< 0.1%
617932.9554 1
 
< 0.1%
174286.9538 1
 
< 0.1%
236.0772375 1
 
< 0.1%
274140.3806 1
 
< 0.1%
11739.93221 1
 
< 0.1%
928815.846 1
 
< 0.1%
Other values (1191) 1191
47.6%
ValueCountFrequency (%)
5.2451017 1
< 0.1%
8.61387853 1
< 0.1%
13.69092254 1
< 0.1%
19.00448501 1
< 0.1%
19.01067912 1
< 0.1%
19.01081377 1
< 0.1%
19.01235643 1
< 0.1%
19.02209356 1
< 0.1%
19.03786565 1
< 0.1%
19.05002063 1
< 0.1%
ValueCountFrequency (%)
2987757.872 1
< 0.1%
2935316.445 1
< 0.1%
2912330.859 1
< 0.1%
2905267.625 1
< 0.1%
2896901.264 1
< 0.1%
2847887.175 1
< 0.1%
2847205.024 1
< 0.1%
2839270.357 1
< 0.1%
2809659.955 1
< 0.1%
2772014.697 1
< 0.1%

artnaive
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1.0
1348 
0.0
1152 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7500
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1348
53.9%
0.0 1152
46.1%

Length

2024-04-10T22:49:11.161690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:11.389209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1348
53.9%
0.0 1152
46.1%

Most occurring characters

ValueCountFrequency (%)
0 3652
48.7%
. 2500
33.3%
1 1348
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
66.7%
Other Punctuation 2500
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3652
73.0%
1 1348
 
27.0%
Other Punctuation
ValueCountFrequency (%)
. 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3652
48.7%
. 2500
33.3%
1 1348
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3652
48.7%
. 2500
33.3%
1 1348
 
18.0%

Label
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
1
2101 
0
399 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2101
84.0%
0 399
 
16.0%

Length

2024-04-10T22:49:11.640951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T22:49:11.871949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2101
84.0%
0 399
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1 2101
84.0%
0 399
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2101
84.0%
0 399
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2101
84.0%
0 399
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2101
84.0%
0 399
 
16.0%

Interactions

2024-04-10T22:48:50.167941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:14.622014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:18.383353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:21.803075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:24.715845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:28.146529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:32.224212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:35.393518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:38.373201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:41.129289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:44.160233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:47.320578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:50.402326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:14.855309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:18.752042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:22.030506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:25.106785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:28.380609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:32.451971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:35.631618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:38.589347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:41.359456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:44.378633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:47.551401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:50.653203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:15.091982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:19.131972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:22.279483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:25.375763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:28.624440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:32.709463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:35.881155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:38.817018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:41.592106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:44.957395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:47.791307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:50.895666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:15.592628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:19.507588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:22.515287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:25.601927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:28.865485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:32.948208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:36.123957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:39.041216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:41.820340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:45.191617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:48.024726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:51.158754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:15.911922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:19.791013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:22.761091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:25.825167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:29.106101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:33.196650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:36.364701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:39.265855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:42.048250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:45.425346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:48.254877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:51.422193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:16.167955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:20.037539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:23.012672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:26.072819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:29.349372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:33.446631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:36.625305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:39.501766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:42.298668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:45.664475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:48.502706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:51.676257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:16.636271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:20.297930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:23.265869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:26.454240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:29.594008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:33.703841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:36.878684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:39.738825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:42.546237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:45.913534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:48.739061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:51.935208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:16.917514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:20.559865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:23.521051image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:26.864014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:29.859579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:33.965311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:37.137504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:39.983058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:42.801734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:46.158482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:48.996384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:52.162946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:17.137450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:20.778157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:23.741106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:27.098161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:31.221442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:34.299423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:37.362655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:40.190895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:43.016831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:46.373521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:49.221537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:52.396430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:17.355753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:20.999244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:23.971279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:27.331118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:31.461294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:34.646749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:37.604733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:40.414109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:43.236596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:46.594934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:49.448440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:52.641006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:17.606577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:21.243153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:24.225213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:27.577592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:31.704732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:34.887099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:37.855222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:40.644075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:43.469092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:46.830618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:49.680311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:52.887455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:17.972468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:21.538509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:24.454362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:27.874000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:31.959511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:35.134285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:38.112086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:40.878715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:43.741416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:47.064785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T22:48:49.919524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-10T22:49:12.106404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Labelageartnaivecd4csf_amt_removedcsf_lactatecsf_lymphdays_artdur_feverdur_headachedur_vischangedur_vomitfevergcsheadachehemoglobinon_artplateletspotassiumsexsodiumtempviralloadvischangevomitwbcweight
Label1.0000.0170.0000.0130.001-0.0830.011-0.0270.021-0.034-0.018-0.0170.0600.2030.0000.0000.0000.0710.0130.0000.063-0.025-0.0090.0510.0520.0430.034
age0.0171.0000.0100.000-0.0170.043-0.010-0.0150.0160.013-0.017-0.0180.0000.0040.0740.0000.0000.0000.0410.0120.057-0.0060.0280.0000.0000.0990.017
artnaive0.0000.0101.0000.0140.046-0.0010.0060.027-0.029-0.022-0.028-0.0550.0270.0100.0120.0000.4700.0480.0170.0000.056-0.0020.0060.0000.0720.0000.022
cd40.0130.0000.0141.000-0.0730.0250.0390.0140.011-0.000-0.008-0.0450.0330.0000.0000.0200.0330.0510.0000.074-0.0100.001-0.0130.0000.0000.0600.000
csf_amt_removed0.001-0.0170.046-0.0731.0000.0160.0170.018-0.058-0.016-0.0340.0500.0730.0890.0760.0000.0000.050-0.0040.023-0.041-0.012-0.0210.0000.0590.0380.066
csf_lactate-0.0830.043-0.0010.0250.0161.0000.0370.023-0.0200.0400.0430.0430.0820.2570.0000.0000.0000.012-0.0060.047-0.0500.0120.0090.0440.0440.0240.034
csf_lymph0.011-0.0100.0060.0390.0170.0371.0000.0500.0270.0340.0440.0020.0000.0000.0000.0000.0570.032-0.0040.021-0.061-0.011-0.0000.0000.0000.0470.000
days_art-0.027-0.0150.0270.0140.0180.0230.0501.000-0.0070.0120.0390.0090.0000.0000.0000.0490.0730.000-0.0040.0320.019-0.006-0.0090.0310.0000.0000.000
dur_fever0.0210.016-0.0290.011-0.058-0.0200.027-0.0071.0000.0950.1170.0560.0510.0510.0710.0000.0520.0020.0190.0370.0340.0120.0080.0320.0390.0000.033
dur_headache-0.0340.013-0.022-0.000-0.0160.0400.0340.0120.0951.0000.3940.2790.0000.0420.0520.0480.0370.0710.0110.0300.0170.011-0.0340.1090.0880.0950.014
dur_vischange-0.018-0.017-0.028-0.008-0.0340.0430.0440.0390.1170.3941.0000.3680.0040.0620.1050.0000.0000.0000.0210.0810.0340.006-0.0360.0410.0980.0420.081
dur_vomit-0.017-0.018-0.055-0.0450.0500.0430.0020.0090.0560.2790.3681.0000.0000.0960.0470.0620.0240.0640.0080.0000.0290.001-0.0370.0800.0660.0340.034
fever0.0600.0000.0270.0330.0730.0820.0000.0000.0510.0000.0040.0001.0000.0670.0070.0000.0180.048-0.0270.032-0.0180.0120.0240.0000.0000.0770.000
gcs0.2030.0040.0100.0000.0890.2570.0000.0000.0510.0420.0620.0960.0671.0000.0320.0000.0200.0300.0310.1300.0280.008-0.0290.0330.1260.0000.100
headache0.0000.0740.0120.0000.0760.0000.0000.0000.0710.0520.1050.0470.0070.0321.0000.0000.0000.000-0.0060.013-0.0130.002-0.0500.0370.0000.0530.046
hemoglobin0.0000.0000.0000.0200.0000.0000.0000.0490.0000.0480.0000.0620.0000.0000.0001.0000.0000.000-0.0210.000-0.000-0.0190.0090.0000.0140.0130.000
on_art0.0000.0000.4700.0330.0000.0000.0570.0730.0520.0370.0000.0240.0180.0200.0000.0001.0000.040-0.0090.019-0.0130.012-0.0090.0000.0350.0000.000
platelets0.0710.0000.0480.0510.0500.0120.0320.0000.0020.0710.0000.0640.0480.0300.0000.0000.0401.000-0.0080.0210.0150.0100.0210.0000.0570.0590.000
potassium0.0130.0410.0170.000-0.004-0.006-0.004-0.0040.0190.0110.0210.008-0.0270.031-0.006-0.021-0.009-0.0081.0000.0180.0130.0150.0030.0000.0000.0000.000
sex0.0000.0120.0000.0740.0230.0470.0210.0320.0370.0300.0810.0000.0320.1300.0130.0000.0190.0210.0181.000-0.050-0.003-0.0280.0090.0640.0000.169
sodium0.0630.0570.056-0.010-0.041-0.050-0.0610.0190.0340.0170.0340.029-0.0180.028-0.013-0.000-0.0130.0150.013-0.0501.000-0.0090.0120.0000.0250.0000.040
temp-0.025-0.006-0.0020.001-0.0120.012-0.011-0.0060.0120.0110.0060.0010.0120.0080.002-0.0190.0120.0100.015-0.003-0.0091.0000.0120.0190.0500.0210.070
viralload-0.0090.0280.006-0.013-0.0210.009-0.000-0.0090.008-0.034-0.036-0.0370.024-0.029-0.0500.009-0.0090.0210.003-0.0280.0120.0121.0000.0320.0000.0000.045
vischange0.0510.0000.0000.0000.0000.0440.0000.0310.0320.1090.0410.0800.0000.0330.0370.0000.0000.0000.0000.0090.0000.0190.0321.0000.0330.0160.000
vomit0.0520.0000.0720.0000.0590.0440.0000.0000.0390.0880.0980.0660.0000.1260.0000.0140.0350.0570.0000.0640.0250.0500.0000.0331.0000.0000.000
wbc0.0430.0990.0000.0600.0380.0240.0470.0000.0000.0950.0420.0340.0770.0000.0530.0130.0000.0590.0000.0000.0000.0210.0000.0160.0001.0000.000
weight0.0340.0170.0220.0000.0660.0340.0000.0000.0330.0140.0810.0340.0000.1000.0460.0000.0000.0000.0000.1690.0400.0700.0450.0000.0000.0001.000

Missing values

2024-04-10T22:48:53.324414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-10T22:48:54.229327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agesexon_artdays_arttempgcsweightfeverdur_feverheadachedur_headachevischangedur_vischangevomitdur_vomitcd4wbchemoglobinplateletssodiumpotassiumcsf_lactatecsf_glucose_mgcsf_lymphcsf_amt_removedviralloadartnaiveLabel
00.00.01.022.036.7861910.00.01.011.0000001.060.00.021.00.014.00.00.00.01.0139.04.0809104.81.075.18453210.01.432357e+061.00
10.01.01.00.035.2822911.01.01.026.4321291.07.00.07.01.07.00.00.01.01.0135.03.2207141.01.075.18453210.03.545953e+050.01
21.00.00.0101.036.7861911.00.00.026.4321290.014.01.06.01.014.00.01.00.01.0134.05.2619701.01.075.18453216.03.545953e+051.01
30.01.00.00.036.7861911.01.00.014.0000001.014.01.03.01.02.00.00.01.01.0124.03.5603703.01.075.18453216.03.545953e+050.01
40.01.01.049.036.7861910.00.00.026.4321291.030.00.05.00.03.00.00.01.01.0128.04.3947123.81.075.18453236.06.667831e+051.01
51.01.00.00.036.7861911.00.01.026.4321291.014.00.07.00.02.00.00.01.01.0128.04.2401805.11.075.18453212.01.900449e+010.01
60.00.00.017.036.1023521.01.00.011.0000001.021.00.07.00.01.00.00.01.01.0130.05.6985164.11.075.18453215.02.991052e+030.01
70.01.00.0919.037.3771931.01.01.02.0000001.07.01.07.00.02.00.00.01.01.0131.04.2950703.11.075.18453225.02.187597e+051.01
80.01.01.00.036.7861911.01.00.026.4321291.060.00.07.00.05.00.00.00.01.0131.04.0599633.71.075.18453222.02.541902e+050.01
90.00.01.0543.036.7861910.01.01.075.0000001.030.00.04.00.02.01.00.01.01.0124.03.7000007.41.075.18453215.01.561214e+041.00
agesexon_artdays_arttempgcsweightfeverdur_feverheadachedur_headachevischangedur_vischangevomitdur_vomitcd4wbchemoglobinplateletssodiumpotassiumcsf_lactatecsf_glucose_mgcsf_lymphcsf_amt_removedviralloadartnaiveLabel
24900.01.00.01092.035.6314971.00.00.026.4321291.014.00.07.01.03.00.00.01.01.0130.03.7000001.01.080.00000011.0354595.269130.01
24910.01.01.00.036.7861911.00.00.026.4321291.093.01.03.01.05.01.00.01.00.0134.04.0599632.21.080.00000010.0354595.269130.01
24921.01.01.00.036.5998661.01.01.026.4321290.014.00.05.01.05.00.00.00.01.0133.03.5503893.81.070.00000010.0354595.269130.01
24930.00.00.00.037.0594561.01.00.026.4321291.06.00.03.00.06.00.00.01.01.0120.04.3215693.81.075.18453224.0346226.169701.00
24940.01.01.01316.036.7861911.01.01.026.4321291.060.01.060.00.060.00.00.01.01.0121.03.7000002.91.075.18453210.0562547.397600.01
24950.00.01.01533.036.7861911.01.01.090.0000001.030.01.032.01.027.00.00.01.01.0137.04.0599631.01.080.00000021.0354595.269130.01
24960.00.01.01140.036.0458081.01.00.026.4321291.04.00.03.00.04.00.00.01.00.0134.03.7000003.21.075.18453215.034874.251101.01
24971.00.00.0224.036.7861911.01.00.02.0000001.07.00.05.00.03.00.00.01.01.0131.04.0599632.21.068.0000001.0354595.269131.01
24980.00.00.0141.036.7861911.00.00.013.0000001.014.01.08.00.05.01.00.00.01.0133.04.0599635.51.075.18453215.0354595.269131.01
24990.00.00.0775.036.7861910.01.00.026.4321291.014.01.07.00.02.00.00.01.01.0131.04.0599634.81.075.18453231.0354595.269131.01